This short paper is the easiest, fastest way
to learn about Truncated Regularized Iteratively Re-weighted Least
Squares (TR-IRLS), my algorithm for fast, parameter-free logistic
regression. TR-IRLS can also be used for any generalized linear
model. This

Proceedings of the 5th International Conference on Data Mining
Machine Learning

in this paper we propose an algorithm that allows to learn a Bayes Net structure from sparse data (e.g., power-law distributed) with over 100,000 variables. we also report time and performance accuracy when applied to several very large datasets

Discriminative probabilistic classifiers have been used successfully on large life-sciences datasets, but high dimensionalities have prohibited the use of nonparametric class probability estimation. This paper explores a method (SLAMDUNK) which addresses

An algorithm for discovering top N strange co-occurences of size 2,3,4, etc Uses ideas of frequent sets, but stratifies them according to a statistically justified hierarchical bayes model, using empirical bayes to find the parameters

We consider the difficult control problem of learing to fly an autonomous helicopter using limited observational data of its dynamics. To that end, we develop policy search techiques that perform well on average with respect to dynamic consistent with our

Proceedings of the International Conference on Robotics and Automation